Device for predictive diagnosis of the current quality of technical production from a technical installation, particularly of the current quality of welding points of a spot welding robot

ABSTRACT

A device for predictively diagnosing the prevailing quality of the technical work result of a technical installation, in particular, the prevailing quality of the welding spots of a welding robot. The device includes a device for cyclically acquiring sets of measured values, whose values influence the desired quality of the work result of the system ,an installation model which uses sets of measured values of the installation to simulate an actual value for the prevailing quality of the work result of the installation, and a device at least for parameterizing the installation model. The parameterizing device includes a data base for storing selected sets of measured values and associated characteristic values, which are a measure for the quality of the work result of the installation, and the device generating and/or optimizing at least the parameters of the installation model by successively evaluating the sets of measured values and the associated characteristic quality values by an iterative optimization.

FIELD OF THE INVENTION

The present invention relates to a device and method for predictivelydiagnosing the prevailing quality of the technical work result of atechnical installation, in particular, the prevailing quality of thewelding spots of a spot-welding robot.

BACKGROUND INFORMATION

In practice, it can be extremely difficult to determine the prevailingquality of the prevailing technical work result of a technicalinstallation, in particular, a production plant. In contrast todetermining physical quantities using measuring techniques, in manycases there are no common direct measuring methods available fordetermining the quality parameters of production results, In some cases,there is success in assembling a highly specialized, complex measuringarrangement which, for instance, is based on radiological,electromagnetic or optical principles or a combination thereof. Manytimes, however, it is still necessary for the prevailing qualityparameters to be subjectively determined by experienced operatingpersonnel, for example, within the framework of a “quality control.”

This produces a multitude of disadvantages. First of all, thedetermination of quality parameters by experienced operating personnelis neither representative nor reproducible. Rather, assessments of thiskind vary even in the short term, depending on the operating personnelemployed and their respective daily condition. Furthermore, operatingpersonnel can generally only carry out evaluations of quality parameterson selected production results of the respective installation by takingrandom samples. A temporary absence or a change of “experiencedoperating personnel”, for example, make it impossible to preventunreproducible assessment variations in the long term, as well.

Secondly, exceptional outlay is required to be able to use the qualityparameters, gained from the assessments by the operating personnel,along the lines of open-loop or closed-loop control engineering in theform of control variables or adjusted setpoint values for influencingthe operational performance of the respective technical installation.Particularly in the case of high-speed, possibly fully automaticproduction plants, it is almost impossible in practice for thecharacteristic quality values, gained from random samples, to be madeusable sufficiently quickly to influence the operational equipment ofthe technical installation.

In an article entitled “Recent Developments and Trends In QualityControl Technology For Resistance Welding, ” by K. Matsayuma, thepossibility of continuously determining specific parameters forresistance welding during a welding described. Also mentioned in thepossibility of determining the parameters with the aid of a neuralnetwork.

In another article entitled “An Intelligent Control System forResistance Spot Welding Using A Neural Network And Fuzzy Logic” by R. W.Messler, an intelligent control system is described, which is based onfuzzy logic and is used for compensating variations and faults duringthe automatic resistance welding operation.

SUMMARY

An object of the present invention is to provide a device and a methodfor predictively diagnosing the prevailing quality of the technical workresult of a technical installation, by which it is possible to determinethe quality of a technical work result in a reproducible way.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1: shows a block diagram of the diagnostic device according to thepresent invention.

FIG. 2: shows a block diagram of a device, designed according to thepresent invention, for predictively diagnosing the prevailing quality ofthe welding spots of a spot-welding robot.

DETAILED DESCRIPTION

The basic design of a device, designed according to the presentinvention, for predictively diagnosing the prevailing quality of thetechnical working result of a technical installation is furtherexplained on the basis of the block diagram in FIG. 1. As a generalprinciple, any technical facility by which solid, liquid and/or gaseousstarting materials are worked, processed or converted into a changed ornew product can be considered a technical installation 1 in which adevice for diagnosing the quality according to the present invention canbe employed.

The predictive diagnostic device according to the present inventionincludes a device 2 for acquiring sets of measured values of technicalinstallation 1. Active measured values, for example, of operationalequipment and materials in the installation whose active values affectthe desired quality of the technical work result at the issue output oftechnical installation 1, are acquired during the operation of theinstallation, i,e., on-line.

The sets of measured values, which are mostly acquired cyclically atpoints of time predefined in a time-discrete manner, are forwarded to aninstallation model 12, and also to a device 3 which is used at least forthe parameterization of installation model 12. Installation model 12uses the active sets of measured values of technical installation 1,preferably available cyclically, to permanently simulate an actual valuefor the prevailing quality of the technical work result of technicalinstallation 1. This actual quality value at the output of installationmodel 12 can be used for many purposes.

In the example of FIG. 1, the actual quality value is compared in acomparator 13 to a predefined setpoint quality value 14. A possiblyresulting deviation in quality can be advantageously forwarded to anadditional processing unit 15. This processing unit can triggersignaling actions 23, informing, e.g., the operating personnel of thecurrently existing deviation in quality. It is particularly advantageousif the active values of the deviation in quality are fed either directlyor in an adapted form to a closed-loop or open-loop control system 24.This control system, in turn, can derive therefrom specific actuatingsignals 25 for technical installation 1, whereby its operating equipmentcan be adapted in such a way that the active actual quality value at theissue of installation model 1 is adapted to the predefined qualitysetpoint value as quickly as possible, and that the deviation in qualityconsequently becomes zero if possible.

From the measured values cyclically acquired by measured-valueacquisition 2, selected sets of measured values are diverted andforwarded to a further device 3 which is used at least forparameterizing installation model 12. In the example of FIG. 1, thisdevice 3 is represented by a dash-lined arrow provided with referencesymbol 22. The selected sets of measured values, after a possibletemporary storage in an additional buffer storage 6 and a possiblesignal processing in a filter and normalization device 7, are fed to a“data base”8. In this context, the sets of measured values are selectedfrom the stream of measured values provided on-line by measured-valueacquisition 2 under the condition that the active characteristic value,existing in the moment when a selectable set of measured values appears,for the quality of the respective work result of the technicalinstallation can be acquired. For example a work result, e.g., aworkpiece from a production plant, which corresponds to a selected setof measured values, may be taken out and analyzed, for example, in a labexamination. The characteristic values 4, which ensue as examinationresults and represent a measure for the respective quality of thetechnical work result of technical installation 1, are also forwarded todata base 8 after a possible intermediate storage in a storage 5. In theexample of FIG. 1, this is represented by a connection provided withreference symbol 11.

In data base 8, the selected sets of measured values and the respectiveappertaining characteristic quality values are systematically filed ingroups. In an evaluator 9, at least the parameters of the installationmodel are generated and/or optimized by a successive evaluation of thesets of measured values 2 and the appertaining characteristic qualityvalues by an iterative optimization. Device 3 for parameterizinginstallation model 12 is advantageously designed in the form of a neuralnetwork. If desired, device 9 can also be designed in such a way that ageneration of the logical structure of the installation model also iscarried out, for example, by iterative evaluation of the individualgroups of related sets of measured values and characteristic qualityvalues.

FIG. 2 shows a schematic representation of a device designed accordingto the present invention for predictively diagnosing the prevailingquality of the welding spots of a spot-welding robot. In this context,the spot-welding device, by way of example, is a technical installation1, whose work results, i.e., the quality of its welding spots, can bedetermined with the aid of the diagnosing device according to thepresent invention. The installation may include at least one automatichandling device 16 for positioning electrode holders 17. With thismeans, welding spots are placed, controlled by a program, on, forexample, a sheet-metal-like work 18 to be welded. The technologicalparameters for classifying the technical quality of welding spots 19 caninclude, for example, the diameter of the raised head part of thewelding spots and the color distribution on their surface, e.g., in thegradations of dark, medium and light gray. For example, thetechnological quality of welding spots can be assigned to the classes“good, adequate, poor and inadequate.”

In the application of FIG. 2, a device 2 for acquiring sets of measuredvalues whose active values influence the desired quality of the weldingspots of the spot-welding robot, is connected to the technicalinstallation. In this context, the sets of measured values include atleast the prevailing values of welding current P1x and of electrode-holder pressure P2x of spot-welding robot 1. The identificationcharacter x in the designations P1x and P2x is intended to indicate thatthe measured values are acquired cyclically at preferably equidistantinstants tx. Therefore, viewed over a longer period of time, chains ofvalues per measuring parameter ensue, i.e., for each of the instants t1,t2, t3 . . . tx, for example, a measured value P11, P12, P13 . . . P1xfor the welding current and, for example, a measured value P21, P22, P23. . . P2x for the electrode-holder pressure. On the one hand, these setsof measured values, acquired on-line, are fed via data line 22 to device3 which is used for generating and parameterizing installation model 12,and, on the other hand, via data line 26 to installation model 12itself.

Device 3 for generating and parameterizing installation model 12 in FIG.2, in turn, has a data base 8 which is used for storing sets of measuredvalues 2 and associated characteristic values, which are a measure forthe respective quality of welding spots 19 of spot-welding robot 16, 17.In the data base, a complete set of measured values P1x, P2x . . .together with an associated characteristic quality value Q1, Q2, Q3 . .. Qx are stored for each acquisition instant t1, t2, t3 . . . tx. In theexample shown in FIG. 2, welding current P1x, electrode-holder pressureP2x, a “rate time” P3x for the increase of the welding current, a“current time” P4x for the duration of the constant value of the weldingcurrent and a “time lag” P5x for the drop of the welding current areacquired as measured values being the technological parameters whichinfluence the quality of welding spots. Accordingly, for eachacquisition instant t1, t2, t3 . . . tx, a complete set of thesemeasured values, i.e., P11, P21, P31, P41, P51 . . . P1x, P2x, P3x, P4x,P5x is stored in the data base together with the appertainingcharacteristic quality value Q1, Q2, Q3 . . . Qx. These groups of valuescan also be referred to as “cases”.

The characteristic quality values belonging to a set of measured valuescan be determined, for example, by a testing person located near thespot-welding robot, or by an automatic testing device which, forexample, is provided with a video camera having an evaluator connectedto it, or by a button test of welding spot specimens within theframework of a destructive workpiece test in a laboratory. In theexample of FIG. 2, the characteristic quality values are input into database 3 via data line 11.

By successively evaluating the sets of measured values kept available inthe data base and the appertaining characteristic quality values, in afollowing evaluator 9 in an “off-line” process, i.e., not in step withthe preferably cyclic online acquisition of the sets of measured values,at least the parameters of the installation model can be generated by aniterative optimization. Depending on the capacity of the algorithm usedfor the iteration, and the structure of the installation model,respectively, it may also be possible that the structure of theinstallation model itself is optimized and adjusted adaptively tochanges in the sets of measured values. For example, the installationmodel can be designed in the form of a “neural network”, or it ispossible to use evolutionary strategies or “cluster processes” for theiteration.

In the example of FIG. 2, the determined parameters are forwarded viadata line 10 to installation model 12. This installation model can now,quasi in the form of an on-line converter, simulate a characteristicvalue Qm, Qm+1, Qm+2, Qm+3. . . for the prevailing quality of thewelding spots of spot-welding robot 16, 17, from the active sets ofmeasured values of technical installation 1 being cyclically forwardedvia data line 26. Therefore, actual values for the quality of thetechnical work result are available at the issue of installation model12, these values being able to be used in closed-loop or open-loopcontrol engineering for influencing the operating mode of thespot-welding robot.

As a function of the number n of measured values provided in themeasured-value acquisition 2, the installation model can be regarded asa dimensional structure of values corresponding to n. In the example ofFIG. 2, such a structure is shown by way of example for an arrangementhaving four measured values P1 . . . P4. Consequently, in thisstructure, ranges of values result for the actual quality value Qm.These are identified in FIG. 2 by diagonal hatchings. Thus, for example,21 designates a “critical range”, characteristic quality values havingunwanted values being assigned to sets of values included therein,according to the prediction by the installation model.

It is decisive for the accuracy of the installation model, and thedetermination of characteristic quality values made possible by it,that, depending on the respective technological process, as manyrelevant measured values of the technical installation as possible arecyclically acquired.

Thus, it is advantageous in the case of spot-welding units that the setsof measured values, whose active values influence the desired quality ofthe welding spots of the spot-welding robot, additionally include valueswhich characterize the pressure build-up at the electrode holders ofspot-welding robot 16. The measured value of electrode-holder pressureP2x of the spot-welding robot is advantageously simulated by measuringthe air pressure in a pneumatic drive of the electrode holders.

Furthermore, the quality of welding spots is particularly influenced byvalues which characterize the curve of the welding current of thespot-welding robot. Therefore, it is advantageous if the sets ofmeasured values include characteristic values for the “rate time” in thecurve of the welding current, i.e., the current rise time, for the“current time” in the curve of the welding current, i.e., the phase ofconstant and maximum welding current, and for the “time lag” in thecurve of the welding current.

Furthermore, the quality of welding spots is particularly influenced byvalues which characterize temperature progressions occurring during thespot-welding operation. Therefore, it is advantageous if the sets ofmeasured values additionally include characteristic values for atemperature of a cooling medium of the welding helmets at the electrodeholders of the spot-welding robot. Finally, values characterizing aninlet and/or a return temperature of a cooling medium for weldinghelmets at the electrode holders of spot-welding robot (16) can permitconclusions about the prevailing quality of the instantaneous technicalwork result.

What is claimed is:
 1. A device for predictively diagnosing a prevailingquality of a technical work result of a technical installation,comprising: a first device cyclically acquiring sets of measured valuesduring operation of the technical installation, the active valuesinfluencing a desired quality of the technical work result of thetechnical installation; an installation model simulating an actual valuefor the prevailing quality of the technical work result of the technicalinstallation using at least one active one of the acquired sets ofmeasured values; and a second device parameterizing the installationmodel, the second device including a database for storing selected onesof the acquired sets of measured values, and characteristic qualityvalues determined as an examination result, the characteristic qualityvalues being a measure for the respective quality of the technical workresult of the technical installation, and an evaluator successivelyevaluating the measured values and the characteristic quality values byan iterative optimization, the evaluator further determining parametersof the installation model.
 2. The device according to claim 1, whereinthe second device includes a neural network.
 3. The device according toclaim 1, wherein the technical installation is a spot-welding robot, thetechnical work result is welding spots of the spot-welding robot, andthe acquired sets of measured values include at least active values of awelding current and active values of a pressure of the electrode-holderof the spot-welding robot.
 4. The device according to claim 3, whereinthe measured value of the pressure of the electrode-holder of thespot-welding robot is simulated by measuring an air pressure in apneumatic drive of the electrode holder.
 5. The device according toclaim 3, wherein the acquired sets of measured values further includevalues which characterize a pressure buildup at the electrode holder ofthe spot-welding robot.
 6. The device according to claim 3, wherein theacquired sets of measured values further include values whichcharacterize a curve of the welding current of the spot-welding robot.7. The device according to claim 3, wherein the acquired sets ofmeasured values further include values which characterize a rate time ina curve of the welding current of the spot-welding robot.
 8. The deviceaccording to claim 3, wherein the acquired sets of measured valuesfurther include values which characterize a time lag in a curve of thewelding current of the spot-welding robot.
 9. The device according toclaim 3, wherein the acquired sets of measured values further includevalues which characterize a current time in a curve of the weldingcurrent of the spot-welding robot.
 10. The device according to claim 3,wherein the acquired sets of measured values further include valueswhich characterize a temperature of a cooling medium for welding helmetsat the electrode holder of the spot-welding robot.
 11. The deviceaccording to claim 3, wherein the acquired sets of measured valuesfurther includes values which characterize at least one of an inlet anda return temperature of a cooling medium for welding helmets at theelectrode holder of the spot-welding robot.
 12. The device according toclaim 1, wherein an actual quality value is provide at an issue outputof the installation model, the actual quality value being compared to apredetermined quality setpoint value in a comparator for determining adeviation in quality.
 13. The device according to claim 12, furthercomprising: a processing unit, the deviation in quality being fed to theprocessing unit for at least one i) triggering a signaling action, ii)signaling the deviation in quality, and iii) deriving actuating signalsfor the technical installation.
 14. A method for predictively diagnosinga prevailing quality of a technical work result of a technicalinstallation, comprising the steps of: cyclically acquiring sets ofmeasured values during an operation of the technical installation, themeasured values including active values which influence a desiredquality of the technical work result of the technical installation;simulating an actual value for the prevailing quality of the technicalwork result of the technical installation using at least one of the setsof measured values; and parameterizing an installation model, includingthe steps of storing in a database selected sets of the measured values,and characteristic quality values determined as an examination result,the characteristic quality values being a measure for the respectivequality of the technical work result of the technical installation, andsuccessively evaluating by an iterative optimization the sets ofmeasured values and associated ones of the characteristic qualityvalues, and determining parameters of the installation model as afunction of the successive evaluations.